56 citations · 91 across the 32 of their papers we have counts for
9 papers · 1 filter
Automatic Pruning Discovery for Large Language Models
Haidong Kang, Lihong Lin, Enneng Yang +2
Large language models (LLMs) have achieved remarkable performance on a wide range of tasks, hindering real-world deployment due to their massive size. Existing pruning methods (e.g…
Model Unmerging: Making Your Models Unmergeable for Secure Model Sharing
Zihao Wang, Enneng Yang, Lu Yin +2
Model merging leverages multiple finetuned expert models to construct a multi-task model with low cost, and is gaining increasing attention. However, as a growing number of finetun…
Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
Chu Zhao, Eneng Yang, Yizhou Dang +3
Heuristic negative sampling enhances recommendation performance by selecting negative samples of varying hardness levels from predefined candidate pools to guide the model toward l…
OptMerge: Unifying Multimodal LLM Capabilities and Modalities via Model Merging
Yongxian Wei, Runxi Cheng, Weike Jin +7
Foundation models update slowly due to resource-intensive training, whereas domain-specific models evolve rapidly between releases. Model merging seeks to combine multiple expert m…
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang +4
Data augmentation has become a promising method of mitigating data sparsity in sequential recommendation. Existing methods generate new yet effective data during model training to…
Hard Negative Sampling via Large Language Models for Recommendation
Chu Zhao, Enneng Yang, Yuting Liu +2
Hard negative sampling improves recommendation performance by accelerating convergence and sharpening the decision boundary. However, most existing methods rely on heuristic strate…
Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product
Pengxiang Lan, Haoyu Xu, Enneng Yang +4
Prompt tuning (PT) offers a cost-effective alternative to fine-tuning large-scale pre-trained language models (PLMs), requiring only a few parameters in soft prompt tokens added be…
Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
Chu Zhao, Enneng Yang, Yuliang Liang +3
The distributionally robust optimization (DRO)-based graph neural network methods improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the model's…
Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging
Anke Tang, Enneng Yang, Li Shen +4
Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their specialized capabilities across different tasks and domains.…